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Record W4309027090 · doi:10.1177/09646639221138416

‘They Just Let Us Rot to Death:’ Anti-Colonialism, Contestation, and Resistance to Reparations for Indian Residential School Abuse

2022· article· en· W4309027090 on OpenAlexfundaboutno aff
Konstantin Petoukhov

Bibliographic record

VenueSocial & Legal Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRedressColonialismForegroundingResistance (ecology)Context (archaeology)State (computer science)PoliticsSociologyPolitical scienceCompensation (psychology)CriminologyGender studiesLawHistoryPsychologySocial psychology

Abstract

fetched live from OpenAlex

In the wake of increasing attention to reparations for settler colonialism in recent years, the politics of refusal and contestation of reparations has remained an underexplored area in socio-legal research. This article addresses this gap by foregrounding the perspectives of the colonised as a focal point to examine the strategies they mobilise to stage resistance to state-sponsored redress and to expose the harmful logics and legacies of ongoing settler colonialism. Strategies of resistance are discussed in the context of the Independent Assessment Process - a financial compensation process designed to provide redress to survivors of the physical and sexual violence they had suffered while attending Canada's Indian Residential Schools. This article explores how survivors disrupted the compensation process to advance an anti-colonial agenda, to politicise the violence, and to compel the settler state to recognise their lived experiences and realities of structural violence in the settler colonial present.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0290.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.387
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2022
Admission routes2
Has abstractyes

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